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We investigate the usage of Large Language Model (LLM) in collecting high-quality data to warm-start Reinforcement Learning (RL) algorithms for learning in some classical Markov Decision Process (MDP) environments.
Tree-based batch mode reinforcement learning
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Batch reinforcement learning
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Overcoming exploration in reinforcement learning with demonstrations
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Kickstarting deep reinforcement learning
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Reinforcement learning: Theory and algorithms
Alekh Agarwal, Nan Jiang, Sham M Kakade, and Wen Sun · 2019
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Unsupervised state representation learning in atari
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, and R Devon Hjelm · 2019
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Soft actor-critic algorithms and applications, 2019
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 2019
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A survey of reinforcement learning informed by natural language
Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, and Tim Rocktäschel · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
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Can language models encode perceptual structure without grounding? a case study in color
Mostafa Abdou, Artur Kulmizev, Daniel Hershcovich, Stella Frank, Ellie Pavlick, and Anders Søgaard · 2021
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Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Representation learning for online and offline rl in low-rank mdps
Masatoshi Uehara, Xuezhou Zhang, and Wen Sun · 2021
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Instabilities of offline rl with pre-trained neural representation
Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov, and Sham Kakade · 2021
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Policy finetuning: Bridging sample-efficient offline and online reinforcement learning
Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, and Yu Bai · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al · 2022
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Lmpriors: Pre-trained language models as task-specific priors
Licong Lin, Yu Bai, and Song Mei · 2023
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Eureka: Human-level reward design via coding large language models
Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
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Text2reward: Automated dense reward function generation for reinforcement learning
Tianbao Xie, Siheng Zhao, Chen Henry Wu, Yitao Liu, Qian Luo, Victor Zhong, Yanchao Yang, and Tao Yu · 2023
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Policy finetuning in reinforcement learning via design of experiments using offline data
Ruiqi Zhang and Andrea Zanette · 2023
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Language agent tree search unifies reasoning acting and planning in language models
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Kristy Choi, Chris Cundy, Sanjari Srivastava, and Stefano Ermon · 2022
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Inner monologue: Embodied reasoning through planning with language models
Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, et al · 2022
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Housekeep: Tidying virtual households using commonsense reasoning
Yash Kant, Arun Ramachandran, Sriram Yenamandra, Igor Gilitschenski, Dhruv Batra, Andrew Szot, and Harsh Agrawal · 2022
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Mapping language models to grounded conceptual spaces
Roma Patel and Ellie Pavlick · 2022
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d3rlpy: An offline deep reinforcement learning library
Takuma Seno and Michita Imai · 2022
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Hybrid rl: Using both offline and online data can make rl efficient
Yuda Song, Yifei Zhou, Ayush Sekhari, J Andrew Bagnell, Akshay Krishnamurthy, and Wen Sun · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao · 2022
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Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang · 2023
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Grounding llms for robot task planning using closed-loop state feedback
Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, and Farshad Khorrami · 2024
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Efficient sequential decision making with large language models
Dingyang Chen, Qi Zhang, and Yinglun Zhu · 2024
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Plan-seq-learn: Language model guided rl for solving long horizon robotics tasks
Murtaza Dalal, Tarun Chiruvolu, Devendra Chaplot, and Ruslan Salakhutdinov · 2024
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Yilun Hao, Yang Zhang, and Chuchu Fan · 2024
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Bench llm deciders with gym translators
Jarvis K · 2024
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Can large language models explore in-context?
Akshay Krishnamurthy, Keegan Harris, Dylan J Foster, Cyril Zhang, and Aleksandrs Slivkins · 2024
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Physgen: Rigid-body physics-grounded image-to-video generation
Shaowei Liu, Zhongzheng Ren, Saurabh Gupta, and Shenlong Wang · 2024
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Evolve: Evaluating and optimizing llms for exploration
Allen Nie, Yi Su, Bo Chang, Jonathan N Lee, Ed H Chi, Quoc V Le, and Minmin Chen · 2024
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Llamagym: Fine-tune llm agents with online reinforcement learning
Rohan Pandey · 2024
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Weihao Tan, Wentao Zhang, Shanqi Liu, Longtao Zheng, Xinrun Wang, and Bo An · 2024
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Gymnasium: A standard interface for reinforcement learning environments
Mark Towers, Ariel Kwiatkowski, Jordan Terry, John U Balis, Gianluca De Cola, Tristan Deleu, Manuel Goulão, Andreas Kallinteris, Markus Krimmel, Arjun KG, et al · 2024
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An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al · 2024
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Toward efficient exploration by large language model agents, 2025
Dilip Arumugam and Thomas L. Griffiths · 2025
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Shrutika Singh, Anton Alyakin, Daniel Alexander Alber, Jaden Stryker, Ai Phuong S Tong, Karl Sangwon, Nicolas Goff, Mathew de la Paz, Miguel Hernandez-Rovira, Ki Yun Park, Eric Claude Leuthardt, and Eric Karl Oermann · 2025
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A survey of robot intelligence with large language models
Hyeongyo Jeong, Haechan Lee, Changwon Kim, and Sungtae Shin · 2076
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